Collaborative Optimization of Game Enemy Design and Network Security Defense Based on Deep Reinforcement Learning


  • Jianshu Liu



Deep Reinforcement Learning; Game Enemy; Network Security Defense; Collaborative Optimization.


The purpose of this paper is to explore the cooperative optimization strategy of game enemy design based on Deep Reinforcement Learning (DRL) and Network Security Defense (NSD). By analyzing the correlation between game enemy design and NSD, this paper puts forward a method of integrating DRL technology to improve the game experience and protect the security of the game system. Firstly, the paper introduces the basic concepts and existing research progress of game enemy design and NSD. Then, the paper introduces the design method of game enemies based on DRL in detail, including the training and behavior generation of enemy agents. Then, the paper discusses the application of DRL in NSD, including network traffic analysis and monitoring and intelligent defense strategy generation. Through experimental design and result analysis, the paper verifies the effectiveness and performance of collaborative optimization strategy, and shows its potential in improving game experience and protecting network security. Finally, the paper summarizes the research results and discusses the future research direction and development trend. This paper provides important reference and guidance for deeply understanding and applying DRL technology to game enemy design and NSD.


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How to Cite

“Collaborative Optimization of Game Enemy Design and Network Security Defense Based on Deep Reinforcement Learning” (2024) Transactions on Computer Science and Intelligent Systems Research, 4, pp. 86–91. doi:10.62051/4xk79k26.

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